Acceptance Sampling

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Statistical procedure used in quality control.
Acceptance sampling involves testing a batch of data to determine if the proportion of units having a particular attribute exceeds a given percentage.
The sampling plan involves three determinations: (1) batch size; (2) sample size; and (3) maximum number of defects that can be uncovered before rejection of the entire batch. This technique permits acceptance or rejection of a batch of merchandise or documents under precisely specified circumstances, thereby ensuring that the auditor does not reject too many acceptable batches. Acceptance sampling is of particular value to the internal auditor who wants continuous control on the quality of clerical work. From acceptance sampling tables, one can select a sampling plan to assure that errors will not be greater than a specified percentage of the batch (tolerable error rate), provided a full check of rejected batches is made. Acceptance sampling can also be used by the internal auditor to inspect the documents flowing through information channels of the organization. Items that can be checked include pricing and mathematical calculations. Acceptance sampling is basically an internal audit tool. It would be very difficult for the external auditor to devise a sampling plan that, while rejecting, say, 90% of unsatisfactory batches, does not also reject a high number of satisfactory batches.

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A statistical measure used in quality control. A company cannot test every one of its products due to either ruining the products, or the volume of products being too large. Acceptance sampling solves this by testing a sample of product for defects. The process involves batch size, sample size and the number of defects acceptable in the batch. This process allows a company to measure the quality of a batch with a specified degree of statistical certainty without having to test every unit of product. The statistical reliability of a sample is generally measured by a t-statistic.

Investopedia Says:
Probability is a key factor in acceptance sampling, but it is not the only factor. If a company makes a million products and tests 10 units with one default, an assumption would be made on probability that 100,000 of the 1,000,000 are defective. However, this could be a grossly inaccurate representation. More reliable conclusions can be made by increasing the batch size higher than 10, and increasing the sample size by doing more than just one test and averaging the results. When done correctly, acceptance sampling is a very effective tool in quality control.

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